robustcov documentation

robustcov is an experimental robust covariance, heavy-tail scatter, anomaly diagnostics, and benchmark-gallery package with a C++/pybind core.

The project is organized around two reader-friendly entry points:

Use-case
gallery

Start from your application

Topic-based gallery: finance/risk, fraud/security, sensors/quality, biomedical/images/embeddings, real ML datasets, and preprocessing.

 Benchmark ranking plot

Start from the evidence

Small-sample heavy-tail ranking, speed comparisons, OpenMP scaling, anomaly baselines, and hard contamination scenarios.
Math
and API

Understand the estimators

FastMCD, Tyler shape, Regularized Tyler, Student-t scatter, Cauchy scatter, diagnostics, and references.

Core ideas

  • FastMCD gives efficient classical robust covariance for separable contamination when n is comfortably larger than p.

  • RegularizedCauchy and StudentTScatter target small-sample, high-dimensional, heavy-tailed covariance problems.

  • Robust-distance diagnostics turn fitted estimators into interpretable anomaly scores, profiles, QQ plots, and reports.

  • Optional OpenMP acceleration improves larger workloads and benchmark/report generation.

User guide

Reference and evidence

Why not focus on MVE?

Minimum-volume ellipsoid estimators are historically important, but the benchmark evidence in this project points to a stronger niche: efficient MCD for separable outliers and regularized heavy-tail scatter for small samples. MVE may become an experimental add-on later, but it is not currently the core differentiator.